




'''simple docstring'''


from dataclasses import dataclass
from typing import Optional

import numpy as np
import torch
import torch.nn as nn

from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block

@dataclass
class      A       (						_a       ):
							lowercase_							     =							42



class      A       (						nn.Module       ):







							def __init__(    self			:    str				,     lowerCAmelCase_			:    Optional[Any]=3				,     lowerCAmelCase_			:    Optional[Any]=3				,     lowerCAmelCase_			:    Dict=("DownEncoderBlock2D",)				,     lowerCAmelCase_			:    int=(64,)				,     lowerCAmelCase_			:    Tuple=2				,     lowerCAmelCase_			:    Optional[int]=32				,     lowerCAmelCase_			:    int="silu"				,     lowerCAmelCase_			:    Optional[Any]=True				,     )     ->				List[str]:


														"""simple docstring"""


														super().__init__()
														_a      	=							layers_per_block

														_a      	=							torch.nn.Convad(
														    lowerCAmelCase_				,     block_out_channels[0]				,     kernel_size=3				,     stride=1				,     padding=1				,     )

														_a      	=							None
														_a      	=							nn.ModuleList([]   )

														# down
														_a      	=							block_out_channels[0]
														for i, down_block_type in enumerate(lowerCAmelCase_   ):
																					_a      	=							output_channel
																					_a      	=							block_out_channels[i]
																					_a      	=							i == len(lowerCAmelCase_   ) - 1

																					_a      	=							get_down_block(
																					    lowerCAmelCase_				,     num_layers=self.layers_per_block				,     in_channels=lowerCAmelCase_				,     out_channels=lowerCAmelCase_				,     add_downsample=not is_final_block				,     resnet_eps=1e-6				,     downsample_padding=0				,     resnet_act_fn=lowerCAmelCase_				,     resnet_groups=lowerCAmelCase_				,     attention_head_dim=lowerCAmelCase_				,     temb_channels=lowerCAmelCase_				,     )
																					self.down_blocks.append(lowerCAmelCase_   )

														# mid
														_a      	=							UNetMidBlockaD(
														    in_channels=block_out_channels[-1]				,     resnet_eps=1e-6				,     resnet_act_fn=lowerCAmelCase_				,     output_scale_factor=1				,     resnet_time_scale_shift='''default'''				,     attention_head_dim=block_out_channels[-1]				,     resnet_groups=lowerCAmelCase_				,     temb_channels=lowerCAmelCase_				,     )

														# out
														_a      	=							nn.GroupNorm(num_channels=block_out_channels[-1]				,     num_groups=lowerCAmelCase_				,     eps=1e-6   )
														_a      	=							nn.SiLU()

														_a      	=							2 * out_channels if double_z else out_channels
														_a      	=							nn.Convad(block_out_channels[-1]				,     lowerCAmelCase_				,     3				,     padding=1   )

														_a      	=							False







							def   __lowerCAmelCase     (    self			:    str				,     lowerCAmelCase_			:    int   )     ->				str:


														"""simple docstring"""


														_a      	=							x
														_a      	=							self.conv_in(lowerCAmelCase_   )

														if self.training and self.gradient_checkpointing:

																					def create_custom_forward(lowerCAmelCase_			:    Tuple   ):
																												def custom_forward(*lowerCAmelCase_			:    Tuple   ):
																																			return module(*lowerCAmelCase_   )

																												return custom_forward

																					# down
																					if is_torch_version('''>='''				,     '''1.11.0'''   ):
																												for down_block in self.down_blocks:
																																			_a      	=							torch.utils.checkpoint.checkpoint(
																																			    create_custom_forward(lowerCAmelCase_   )				,     lowerCAmelCase_				,     use_reentrant=lowerCAmelCase_   )
																												# middle
																												_a      	=							torch.utils.checkpoint.checkpoint(
																												    create_custom_forward(self.mid_block   )				,     lowerCAmelCase_				,     use_reentrant=lowerCAmelCase_   )
																					else:
																												for down_block in self.down_blocks:
																																			_a      	=							torch.utils.checkpoint.checkpoint(create_custom_forward(lowerCAmelCase_   )				,     lowerCAmelCase_   )
																												# middle
																												_a      	=							torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block   )				,     lowerCAmelCase_   )

														else:
																					# down
																					for down_block in self.down_blocks:
																												_a      	=							down_block(lowerCAmelCase_   )

																					# middle
																					_a      	=							self.mid_block(lowerCAmelCase_   )

														# post-process
														_a      	=							self.conv_norm_out(lowerCAmelCase_   )
														_a      	=							self.conv_act(lowerCAmelCase_   )
														_a      	=							self.conv_out(lowerCAmelCase_   )

														return sample



class      A       (						nn.Module       ):







							def __init__(    self			:    int				,     lowerCAmelCase_			:    Optional[Any]=3				,     lowerCAmelCase_			:    Union[str, Any]=3				,     lowerCAmelCase_			:    Union[str, Any]=("UpDecoderBlock2D",)				,     lowerCAmelCase_			:    List[str]=(64,)				,     lowerCAmelCase_			:    Dict=2				,     lowerCAmelCase_			:    Optional[int]=32				,     lowerCAmelCase_			:    Union[str, Any]="silu"				,     lowerCAmelCase_			:    Dict="group"				,     )     ->				Dict:


														"""simple docstring"""


														super().__init__()
														_a      	=							layers_per_block

														_a      	=							nn.Convad(
														    lowerCAmelCase_				,     block_out_channels[-1]				,     kernel_size=3				,     stride=1				,     padding=1				,     )

														_a      	=							None
														_a      	=							nn.ModuleList([]   )

														_a      	=							in_channels if norm_type == '''spatial''' else None

														# mid
														_a      	=							UNetMidBlockaD(
														    in_channels=block_out_channels[-1]				,     resnet_eps=1e-6				,     resnet_act_fn=lowerCAmelCase_				,     output_scale_factor=1				,     resnet_time_scale_shift='''default''' if norm_type == '''group''' else norm_type				,     attention_head_dim=block_out_channels[-1]				,     resnet_groups=lowerCAmelCase_				,     temb_channels=lowerCAmelCase_				,     )

														# up
														_a      	=							list(reversed(lowerCAmelCase_   )   )
														_a      	=							reversed_block_out_channels[0]
														for i, up_block_type in enumerate(lowerCAmelCase_   ):
																					_a      	=							output_channel
																					_a      	=							reversed_block_out_channels[i]

																					_a      	=							i == len(lowerCAmelCase_   ) - 1

																					_a      	=							get_up_block(
																					    lowerCAmelCase_				,     num_layers=self.layers_per_block + 1				,     in_channels=lowerCAmelCase_				,     out_channels=lowerCAmelCase_				,     prev_output_channel=lowerCAmelCase_				,     add_upsample=not is_final_block				,     resnet_eps=1e-6				,     resnet_act_fn=lowerCAmelCase_				,     resnet_groups=lowerCAmelCase_				,     attention_head_dim=lowerCAmelCase_				,     temb_channels=lowerCAmelCase_				,     resnet_time_scale_shift=lowerCAmelCase_				,     )
																					self.up_blocks.append(lowerCAmelCase_   )
																					_a      	=							output_channel

														# out
														if norm_type == "spatial":
																					_a      	=							SpatialNorm(block_out_channels[0]				,     lowerCAmelCase_   )
														else:
																					_a      	=							nn.GroupNorm(num_channels=block_out_channels[0]				,     num_groups=lowerCAmelCase_				,     eps=1e-6   )
														_a      	=							nn.SiLU()
														_a      	=							nn.Convad(block_out_channels[0]				,     lowerCAmelCase_				,     3				,     padding=1   )

														_a      	=							False







							def   __lowerCAmelCase     (    self			:    Optional[int]				,     lowerCAmelCase_			:    Any				,     lowerCAmelCase_			:    Union[str, Any]=None   )     ->				Any:


														"""simple docstring"""


														_a      	=							z
														_a      	=							self.conv_in(lowerCAmelCase_   )

														_a      	=							next(iter(self.up_blocks.parameters()   )   ).dtype
														if self.training and self.gradient_checkpointing:

																					def create_custom_forward(lowerCAmelCase_			:    Dict   ):
																												def custom_forward(*lowerCAmelCase_			:    Tuple   ):
																																			return module(*lowerCAmelCase_   )

																												return custom_forward

																					if is_torch_version('''>='''				,     '''1.11.0'''   ):
																												# middle
																												_a      	=							torch.utils.checkpoint.checkpoint(
																												    create_custom_forward(self.mid_block   )				,     lowerCAmelCase_				,     lowerCAmelCase_				,     use_reentrant=lowerCAmelCase_   )
																												_a      	=							sample.to(lowerCAmelCase_   )

																												# up
																												for up_block in self.up_blocks:
																																			_a      	=							torch.utils.checkpoint.checkpoint(
																																			    create_custom_forward(lowerCAmelCase_   )				,     lowerCAmelCase_				,     lowerCAmelCase_				,     use_reentrant=lowerCAmelCase_   )
																					else:
																												# middle
																												_a      	=							torch.utils.checkpoint.checkpoint(
																												    create_custom_forward(self.mid_block   )				,     lowerCAmelCase_				,     lowerCAmelCase_   )
																												_a      	=							sample.to(lowerCAmelCase_   )

																												# up
																												for up_block in self.up_blocks:
																																			_a      	=							torch.utils.checkpoint.checkpoint(create_custom_forward(lowerCAmelCase_   )				,     lowerCAmelCase_				,     lowerCAmelCase_   )
														else:
																					# middle
																					_a      	=							self.mid_block(lowerCAmelCase_				,     lowerCAmelCase_   )
																					_a      	=							sample.to(lowerCAmelCase_   )

																					# up
																					for up_block in self.up_blocks:
																												_a      	=							up_block(lowerCAmelCase_				,     lowerCAmelCase_   )

        # post-process
														if latent_embeds is None:
																					_a      	=							self.conv_norm_out(lowerCAmelCase_   )
														else:
																					_a      	=							self.conv_norm_out(lowerCAmelCase_				,     lowerCAmelCase_   )
														_a      	=							self.conv_act(lowerCAmelCase_   )
														_a      	=							self.conv_out(lowerCAmelCase_   )

														return sample



class      A       (						nn.Module       ):







							def __init__(    self			:    Optional[Any]				,     lowerCAmelCase_			:    Union[str, Any]				,     lowerCAmelCase_			:    Any				,     lowerCAmelCase_			:    Any				,     lowerCAmelCase_			:    int=None				,     lowerCAmelCase_			:    Tuple="random"				,     lowerCAmelCase_			:    int=False				,     lowerCAmelCase_			:    Optional[Any]=True   )     ->				List[Any]:


														"""simple docstring"""


														super().__init__()
														_a      	=							n_e
														_a      	=							vq_embed_dim
														_a      	=							beta
														_a      	=							legacy

														_a      	=							nn.Embedding(self.n_e				,     self.vq_embed_dim   )
														self.embedding.weight.data.uniform_(-1.0 / self.n_e				,     1.0 / self.n_e   )

														_a      	=							remap
														if self.remap is not None:
																					self.register_buffer('''used'''				,     torch.tensor(np.load(self.remap   )   )   )
																					_a      	=							self.used.shape[0]
																					_a      	=							unknown_index  # "random" or "extra" or integer
																					if self.unknown_index == "extra":
																												_a      	=							self.re_embed
																												_a      	=							self.re_embed + 1
																					print(
																					    F'Remapping {self.n_e} indices to {self.re_embed} indices. '
																					    F'Using {self.unknown_index} for unknown indices.'   )
														else:
																					_a      	=							n_e

														_a      	=							sane_index_shape







							def   __lowerCAmelCase     (    self			:    int				,     lowerCAmelCase_			:    List[Any]   )     ->				List[str]:


														"""simple docstring"""


														_a      	=							inds.shape
														assert len(lowerCAmelCase_   ) > 1
														_a      	=							inds.reshape(ishape[0]				,     -1   )
														_a      	=							self.used.to(lowerCAmelCase_   )
														_a      	=							(inds[:, :, None] == used[None, None, ...]).long()
														_a      	=							match.argmax(-1   )
														_a      	=							match.sum(2   ) < 1
														if self.unknown_index == "random":
																					_a      	=							torch.randint(0				,     self.re_embed				,     size=new[unknown].shape   ).to(device=new.device   )
														else:
																					_a      	=							self.unknown_index
														return new.reshape(lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Tuple				,     lowerCAmelCase_			:    Optional[Any]   )     ->				str:


														"""simple docstring"""


														_a      	=							inds.shape
														assert len(lowerCAmelCase_   ) > 1
														_a      	=							inds.reshape(ishape[0]				,     -1   )
														_a      	=							self.used.to(lowerCAmelCase_   )
														if self.re_embed > self.used.shape[0]:  # extra token
																					_a      	=							0  # simply set to zero
														_a      	=							torch.gather(used[None, :][inds.shape[0] * [0], :]				,     1				,     lowerCAmelCase_   )
														return back.reshape(lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Optional[Any]				,     lowerCAmelCase_			:    Dict   )     ->				int:


														"""simple docstring"""


														_a      	=							z.permute(0				,     2				,     3				,     1   ).contiguous()
														_a      	=							z.view(-1				,     self.vq_embed_dim   )

														# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
														_a      	=							torch.argmin(torch.cdist(lowerCAmelCase_				,     self.embedding.weight   )				,     dim=1   )

														_a      	=							self.embedding(lowerCAmelCase_   ).view(z.shape   )
														_a      	=							None
														_a      	=							None

														# compute loss for embedding
														if not self.legacy:
																					_a      	=							self.beta * torch.mean((z_q.detach() - z) ** 2   ) + torch.mean((z_q - z.detach()) ** 2   )
														else:
																					_a      	=							torch.mean((z_q.detach() - z) ** 2   ) + self.beta * torch.mean((z_q - z.detach()) ** 2   )

														# preserve gradients
														_a      	=							z + (z_q - z).detach()

														# reshape back to match original input shape
														_a      	=							z_q.permute(0				,     3				,     1				,     2   ).contiguous()

														if self.remap is not None:
																					_a      	=							min_encoding_indices.reshape(z.shape[0]				,     -1   )  # add batch axis
																					_a      	=							self.remap_to_used(lowerCAmelCase_   )
																					_a      	=							min_encoding_indices.reshape(-1				,     1   )  # flatten

														if self.sane_index_shape:
																					_a      	=							min_encoding_indices.reshape(z_q.shape[0]				,     z_q.shape[2]				,     z_q.shape[3]   )

														return z_q, loss, (perplexity, min_encodings, min_encoding_indices)







							def   __lowerCAmelCase     (    self			:    Any				,     lowerCAmelCase_			:    int				,     lowerCAmelCase_			:    Tuple   )     ->				List[Any]:


														"""simple docstring"""


														if self.remap is not None:
																					_a      	=							indices.reshape(shape[0]				,     -1   )  # add batch axis
																					_a      	=							self.unmap_to_all(lowerCAmelCase_   )
																					_a      	=							indices.reshape(-1   )  # flatten again

														# get quantized latent vectors
														_a      	=							self.embedding(lowerCAmelCase_   )

														if shape is not None:
																					_a      	=							z_q.view(lowerCAmelCase_   )
																					# reshape back to match original input shape
																					_a      	=							z_q.permute(0				,     3				,     1				,     2   ).contiguous()

														return z_q



class      A       (						_a       ):







							def __init__(    self			:    str				,     lowerCAmelCase_			:    int				,     lowerCAmelCase_			:    Tuple=False   )     ->				Optional[Any]:


														"""simple docstring"""


														_a      	=							parameters
														_a	,				_a      	=							torch.chunk(lowerCAmelCase_				,     2				,     dim=1   )
														_a      	=							torch.clamp(self.logvar				,     -3_0.0				,     2_0.0   )
														_a      	=							deterministic
														_a      	=							torch.exp(0.5 * self.logvar   )
														_a      	=							torch.exp(self.logvar   )
														if self.deterministic:
																					_a      	=							_a      	=							torch.zeros_like(
																					    self.mean				,     device=self.parameters.device				,     dtype=self.parameters.dtype   )







							def   __lowerCAmelCase     (    self			:    Tuple				,     lowerCAmelCase_			:    Optional[torch.Generator] = None   )     ->				torch.FloatTensor:


														"""simple docstring"""


														_a      	=							randn_tensor(
														    self.mean.shape				,     generator=lowerCAmelCase_				,     device=self.parameters.device				,     dtype=self.parameters.dtype   )
														_a      	=							self.mean + self.std * sample
														return x







							def   __lowerCAmelCase     (    self			:    Any				,     lowerCAmelCase_			:    List[str]=None   )     ->				int:


														"""simple docstring"""


														if self.deterministic:
																					return torch.Tensor([0.0]   )
														else:
																					if other is None:
																												return 0.5 * torch.sum(torch.pow(self.mean				,     2   ) + self.var - 1.0 - self.logvar				,     dim=[1, 2, 3]   )
																					else:
																												return 0.5 * torch.sum(
																												    torch.pow(self.mean - other.mean				,     2   ) / other.var
																												    + self.var / other.var
																												    - 1.0
																												    - self.logvar
																												    + other.logvar				,     dim=[1, 2, 3]				,     )







							def   __lowerCAmelCase     (    self			:    Optional[Any]				,     lowerCAmelCase_			:    Any				,     lowerCAmelCase_			:    List[str]=[1, 2, 3]   )     ->				Any:


														"""simple docstring"""


														if self.deterministic:
																					return torch.Tensor([0.0]   )
														_a      	=							np.log(2.0 * np.pi   )
														return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean				,     2   ) / self.var				,     dim=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    int   )     ->				Optional[Any]:


														"""simple docstring"""


														return self.mean


"""simple docstring"""



import argparse
import json
import os
import re
import shutil

import torch

from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

SCREAMING_SNAKE_CASE__:Union[str, Any]									=			2






class  snake_case__      :


 def __init__( self    ,		*,  # begin keyword-only arguments
     lowerCamelCase="<s>"    ,		lowerCamelCase="<pad>"    ,		lowerCamelCase="</s>"    ,		lowerCamelCase="<unk>"    ,		lowerCamelCase=None    ,		):
        __a ,   __a ,   __a ,   __a       =    bos, unk, pad, eos
        __a       =    []
        __a       =    []
        __a       =    {}
        __a       =    self.add_symbol(lowerCamelCase						)
        __a       =    self.add_symbol(lowerCamelCase						)
        __a       =    self.add_symbol(lowerCamelCase						)
        __a       =    self.add_symbol(lowerCamelCase						)
        if extra_special_symbols:
               for s in extra_special_symbols:
                      self.add_symbol(lowerCamelCase						)
        __a       =    len(self.symbols						)


 def __eq__( self    ,		lowerCamelCase						):
        return self.indices == other.indices


 def __getitem__( self    ,		lowerCamelCase						):
        if idx < len(self.symbols						):
               return self.symbols[idx]
        return self.unk_word


 def __len__( self						):
        return len(self.symbols						)


 def __contains__( self    ,		lowerCamelCase						):
        return sym in self.indices


 @classmethod
 def 						a__   ( cls    ,		lowerCamelCase						):
        __a       =    cls()
        d.add_from_file(lowerCamelCase						)
        return d


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase=1    ,		lowerCamelCase=False						):
        if word in self.indices and not overwrite:
               __a       =    self.indices[word]
               __a       =    self.count[idx] + n
               return idx
        else:
               __a       =    len(self.symbols						)
               __a       =    idx
               self.symbols.append(lowerCamelCase						)
               self.count.append(lowerCamelCase						)
               return idx


 def 						a__   ( self    ,		lowerCamelCase						):
        return 0






 def 						a__   ( self    ,		lowerCamelCase						):
        if isinstance(lowerCamelCase    ,		lowerCamelCase						):
               try:
                      with open(lowerCamelCase    ,		"r"    ,		encoding="utf-8"						) as fd:
                             self.add_from_file(lowerCamelCase						)
               except FileNotFoundError as fnfe:
                      raise fnfe
               except UnicodeError:
                      raise Exception("Incorrect encoding detected in {}, please rebuild the dataset".format(lowerCamelCase						)						)
               return

        __a       =    f.readlines()
        __a       =    self._load_meta(lowerCamelCase						)

        for line in lines[indices_start_line:]:
               try:
                      __a ,   __a       =    line.rstrip().rsplit(" "    ,		1						)
                      if field == "#fairseq:overwrite":
                             __a       =    True
                             __a ,   __a       =    line.rsplit(" "    ,		1						)
                      else:
                             __a       =    False
                      __a       =    int(lowerCamelCase						)
                      __a       =    line
                      if word in self and not overwrite:
                             raise RuntimeError(
                                 "Duplicate word found when loading Dictionary: '{}'. "
                                 "Duplicate words can overwrite earlier ones by adding the "
                                 "#fairseq:overwrite flag at the end of the corresponding row "
                                 "in the dictionary file. If using the Camembert model, please "
                                 "download an updated copy of the model file.".format(lowerCamelCase						)						)
                      self.add_symbol(lowerCamelCase    ,		n=lowerCamelCase    ,		overwrite=lowerCamelCase						)
               except ValueError:
                      raise ValueError("Incorrect dictionary format, expected '<token> <cnt> [flags]'"						)



def 	_lowerCamelCase(	a				):
       # (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
       # e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
       __a       =    dict((re.sub(R"@@$"   ,    ""   ,    a				), v) if k.endswith("@@"				) else (re.sub(R"$"   ,    "</w>"   ,    a				), v) for k, v in d.items()				)
       __a       =    "<s> <pad> </s> <unk>".split()
       # restore the special tokens
       for k in keep_keys:
              del da[F"{k}</w>"]
              __a       =    d[k]  # restore
       return da



def 	_lowerCamelCase(	a   ,    a				):
       # prep
       if not os.path.exists(a				):
              raise ValueError(F"path {biogpt_checkpoint_path} does not exist!"				)
       os.makedirs(a   ,    exist_ok=a				)
       print(F"Writing results to {pytorch_dump_folder_path}"				)

       # handle various types of models

       __a       =    os.path.join(a   ,    "checkpoint.pt"				)
       if not os.path.isfile(a				):
              raise ValueError(F"path to the file {checkpoint_file} does not exist!"				)
       __a       =    torch.load(a   ,    map_location="cpu"				)

       __a       =    chkpt["cfg"]["model"]

       # dicts
       __a       =    os.path.join(a   ,    "dict.txt"				)
       if not os.path.isfile(a				):
              raise ValueError(F"path to the file {dict_file} does not exist!"				)
       __a       =    Dictionary.load(a				)
       __a       =    rewrite_dict_keys(src_dict.indices				)
       __a       =    len(a				)
       __a       =    os.path.join(a   ,    VOCAB_FILES_NAMES["vocab_file"]				)
       print(F"Generating {src_vocab_file} of {src_vocab_size} records"				)
       with open(a   ,    "w"   ,    encoding="utf-8"				) as f:
              f.write(json.dumps(a   ,    ensure_ascii=a   ,    indent=a				)				)

       # merges_file (bpecodes)
       __a       =    os.path.join(a   ,    "bpecodes"				)
       if not os.path.isfile(a				):
              raise ValueError(F"path to the file {bpecodes_file} does not exist!"				)

       __a       =    os.path.join(a   ,    VOCAB_FILES_NAMES["merges_file"]				)
       shutil.copyfile(a   ,    a				)

       # model config
       __a       =    os.path.join(a   ,    "config.json"				)

       __a       =    {
           "activation_dropout": args["activation_dropout"],
           "architectures": ["BioGptForCausalLM"],
           "attention_probs_dropout_prob": args["attention_dropout"],
           "bos_token_id": 0,
           "eos_token_id": 2,
           "hidden_act": args["activation_fn"],
           "hidden_dropout_prob": args["dropout"],
           "hidden_size": args["decoder_embed_dim"],
           "initializer_range": 0.02,
           "intermediate_size": args["decoder_ffn_embed_dim"],
           "layer_norm_eps": 1E-1_2,
           "layerdrop": args["decoder_layerdrop"],
           "max_position_embeddings": args["max_target_positions"],
           "model_type": "biogpt",
           "num_attention_heads": args["decoder_attention_heads"],
           "num_hidden_layers": args["decoder_layers"],
           "pad_token_id": 1,
           "scale_embedding": not args["no_scale_embedding"],
           "tie_word_embeddings": args["share_decoder_input_output_embed"],
           "vocab_size": src_vocab_size,
       }

       # good hparam defaults to start with

       print(F"Generating {biogpt_model_config_file}"				)
       with open(a   ,    "w"   ,    encoding="utf-8"				) as f:
              f.write(json.dumps(a   ,    ensure_ascii=a   ,    indent=a				)				)

       # tokenizer config
       __a       =    os.path.join(a   ,    a				)

       __a       =    {
           "bos_token": "<s>",
           "eos_token": "</s>",
           "model_max_length": 1_0_2_4,
           "pad_token": "<pad>",
           "special_tokens_map_file": None,
           "tokenizer_class": "BioGptTokenizer",
           "unk_token": "<unk>",
       }

       print(F"Generating {biogpt_tokenizer_config_file}"				)
       with open(a   ,    "w"   ,    encoding="utf-8"				) as f:
              f.write(json.dumps(a   ,    ensure_ascii=a   ,    indent=a				)				)

       # model
       __a       =    chkpt["model"]

       # remove unneeded keys
       __a       =    [
           "decoder.version",
       ]
       for k in ignore_keys:
              model_state_dict.pop(a   ,    a				)

       __a       =    list(model_state_dict.keys()				)
       for layer_name in layer_names:
              if layer_name.endswith("output_projection.weight"				):
                     __a       =    model_state_dict.pop(a				)
              else:
                     __a       =    model_state_dict.pop(a				)

       __a       =    BioGptConfig.from_pretrained(a				)
       __a       =    BioGptForCausalLM(a				)

       # check that it loads ok
       model_new.load_state_dict(a				)

       # save
       __a       =    os.path.join(a   ,    a				)
       print(F"Generating {pytorch_weights_dump_path}"				)
       torch.save(a   ,    a				)

       print("Conversion is done!"				)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__:Any									=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--biogpt_checkpoint_path""",
       default=None,
       type=str,
       required=True,
       help=(
           """Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"""
           """ bpecodes, etc."""
       ),
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
   )
   SCREAMING_SNAKE_CASE__:Any									=			parser.parse_args()
   convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)
